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Hidden Markov Models

Overview of attention for book
Cover of 'Hidden Markov Models'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction to Hidden Markov Models and Its Applications in Biology
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    Chapter 2 HMMs in Protein Fold Classification
  4. Altmetric Badge
    Chapter 3 Application of Hidden Markov Models in Biomolecular Simulations
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    Chapter 4 Predicting Beta Barrel Transmembrane Proteins Using HMMs
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    Chapter 5 Predicting Alpha Helical Transmembrane Proteins Using HMMs
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    Chapter 6 Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization
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    Chapter 7 Analyzing Single Molecule FRET Trajectories Using HMM
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    Chapter 8 Modelling ChIP-seq Data Using HMMs
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    Chapter 9 Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence
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    Chapter 10 Computationally Tractable Multivariate HMM in Genome-Wide Mapping Studies
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    Chapter 11 Hidden Markov Models in Population Genomics
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    Chapter 12 Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches
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    Chapter 13 Finding RNA–Protein Interaction Sites Using HMMs
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    Chapter 14 Automated Estimation of Mouse Social Behaviors Based on a Hidden Markov Model
  16. Altmetric Badge
    Chapter 15 Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications
Attention for Chapter 9: Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence
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  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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Chapter title
Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence
Chapter number 9
Book title
Hidden Markov Models
Published in
Methods in molecular biology, February 2017
DOI 10.1007/978-1-4939-6753-7_9
Pubmed ID
28224495
Book ISBNs
978-1-4939-6751-3, 978-1-4939-6753-7
Authors

Jiawen Bian, Xiaobo Zhou

Editors

David R. Westhead, M. S. Vijayabaskar

Abstract

The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Hidden Markov models (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NGS are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNV detection capability in the regulatory regions of the genome. A specific HMM is developed for this purpose to infer the genotype for each position on the genome by incorporating the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The procedure and the implementation of the algorithm is presented in detail for understanding and programming.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 30%
Student > Master 2 20%
Student > Bachelor 1 10%
Unknown 4 40%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 20%
Agricultural and Biological Sciences 1 10%
Computer Science 1 10%
Social Sciences 1 10%
Chemistry 1 10%
Other 0 0%
Unknown 4 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 February 2018.
All research outputs
#13,541,902
of 22,955,959 outputs
Outputs from Methods in molecular biology
#3,648
of 13,137 outputs
Outputs of similar age
#162,281
of 311,194 outputs
Outputs of similar age from Methods in molecular biology
#51
of 266 outputs
Altmetric has tracked 22,955,959 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,137 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,194 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 266 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.

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